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Improve the permutation

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Desire NUENTSA W. 2012-07-27 16:38:20 +02:00
parent c0fa5811ec
commit ce30d50e3e
3 changed files with 39 additions and 12 deletions
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12
Eigen/src/SparseCore/SparseMatrix.h
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@ -477,6 +477,18 @@ class SparseMatrix
m_data.squeeze(); m_data.squeeze();
} }
/** Turns the matrix into the uncompressed mode */
void Uncompress()
{
if(m_innerNonZeros != 0)
return;
m_innerNonZeros = new Index[m_outerSize];
for (int i = 0; i < m_outerSize; i++)
{
m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i];
}
}
/** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerence \a epsilon */ /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerence \a epsilon */
void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision()) void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
{ {

24
Eigen/src/SparseLU/SparseLU.h
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@ -346,8 +346,17 @@ void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
// Apply the permutation to the column of the input matrix // Apply the permutation to the column of the input matrix
m_mat = mat * m_perm_c.inverse(); //FIXME It should be less expensive here to permute only the structural pattern of the matrix // m_mat = mat * m_perm_c.inverse(); //FIXME It should be less expensive here to permute only the structural pattern of the matrix
//First copy the whole input matrix.
m_mat = mat;
m_mat.Uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used.
//Then, permute only the column pointers
for (int i = 0; i < mat.cols(); i++)
{
m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = mat.outerIndexPtr()[i];
m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = mat.outerIndexPtr()[i+1] - mat.outerIndexPtr()[i];
}
// Compute the column elimination tree of the permuted matrix // Compute the column elimination tree of the permuted matrix
if (m_etree.size() == 0) m_etree.resize(m_mat.cols()); if (m_etree.size() == 0) m_etree.resize(m_mat.cols());
@ -424,8 +433,15 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
// Apply the column permutation computed in analyzepattern() // Apply the column permutation computed in analyzepattern()
m_mat = matrix * m_perm_c.inverse(); // m_mat = matrix * m_perm_c.inverse();
m_mat.makeCompressed(); m_mat = matrix;
m_mat.Uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers.
//Then, permute only the column pointers
for (int i = 0; i < matrix.cols(); i++)
{
m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = matrix.outerIndexPtr()[i];
m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = matrix.outerIndexPtr()[i+1] - matrix.outerIndexPtr()[i];
}
int m = m_mat.rows(); int m = m_mat.rows();
int n = m_mat.cols(); int n = m_mat.cols();
@ -504,7 +520,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
// Factorize the relaxed supernode(jcol:kcol) // Factorize the relaxed supernode(jcol:kcol)
// First, determine the union of the row structure of the snode // First, determine the union of the row structure of the snode
info = LU_snode_dfs(jcol, kcol, m_mat.innerIndexPtr(), m_mat.outerIndexPtr(), xprune, marker, m_glu); info = LU_snode_dfs(jcol, kcol, m_mat, xprune, marker, m_glu);
if ( info ) if ( info )
{ {
std::cerr << "MEMORY ALLOCATION FAILED IN SNODE_DFS() \n"; std::cerr << "MEMORY ALLOCATION FAILED IN SNODE_DFS() \n";

13
Eigen/src/SparseLU/SparseLU_snode_dfs.h
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@ -57,8 +57,8 @@
* \param marker (in/out) working vector * \param marker (in/out) working vector
* \return 0 on success, > 0 size of the memory when memory allocation failed * \return 0 on success, > 0 size of the memory when memory allocation failed
*/ */
template <typename IndexVector, typename ScalarVector> template <typename MatrixType, typename IndexVector, typename ScalarVector>
int LU_snode_dfs(const int jcol, const int kcol, const typename IndexVector::Scalar* asub, const typename IndexVector::Scalar* colptr, IndexVector& xprune, IndexVector& marker, LU_GlobalLU_t<IndexVector, ScalarVector>& glu) int LU_snode_dfs(const int jcol, const int kcol,const MatrixType& mat, IndexVector& xprune, IndexVector& marker, LU_GlobalLU_t<IndexVector, ScalarVector>& glu)
{ {
typedef typename IndexVector::Scalar Index; typedef typename IndexVector::Scalar Index;
IndexVector& xsup = glu.xsup; IndexVector& xsup = glu.xsup;
@ -69,14 +69,13 @@
int mem; int mem;
Index nsuper = ++supno(jcol); // Next available supernode number Index nsuper = ++supno(jcol); // Next available supernode number
int nextl = xlsub(jcol); //Index of the starting location of the jcol-th column in lsub int nextl = xlsub(jcol); //Index of the starting location of the jcol-th column in lsub
int i,k;
int krow,kmark; int krow,kmark;
for (i = jcol; i <=kcol; i++) for (int i = jcol; i <=kcol; i++)
{ {
// For each nonzero in A(*,i) // For each nonzero in A(*,i)
for (k = colptr[i]; k < colptr[i+1]; k++) for (typename MatrixType::InnerIterator it(mat, i); it; ++it)
{ {
krow = asub[k]; krow = it.row();
kmark = marker(krow); kmark = marker(krow);
if ( kmark != kcol ) if ( kmark != kcol )
{ {
@ -105,7 +104,7 @@
Index ifrom, ito = nextl; Index ifrom, ito = nextl;
for (ifrom = xlsub(jcol); ifrom < nextl;) for (ifrom = xlsub(jcol); ifrom < nextl;)
lsub(ito++) = lsub(ifrom++); lsub(ito++) = lsub(ifrom++);
for (i = jcol+1; i <=kcol; i++) xlsub(i) = nextl; for (int i = jcol+1; i <=kcol; i++) xlsub(i) = nextl;
nextl = ito; nextl = ito;
} }
xsup(nsuper+1) = kcol + 1; // Start of next available supernode xsup(nsuper+1) = kcol + 1; // Start of next available supernode